On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics

• Published in: Scientific reports Vol. 14; no. 1; pp. 16795 - 13
• Main Authors: Gupta, K. K., Barman, S., Dey, S., Naskar, S., Mukhopadhyay, T.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22-07-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/166/984; 639/166/988; AlCoCrFeNi; Alloys; Bayesian analysis; Computer applications; Energy dissipation; High entropy alloys; High-velocity impact; Humanities and Social Sciences; Impact analysis; Kinetic energy; Learning algorithms; Machine learning; Machine learning assisted molecular dynamics simulation; Mathematical models; Mechanical properties; multidisciplinary; Nonparametric kernel-based probabilistic machine learning; Optimum HEAs with conflicting objectives; Science; Science (multidisciplinary); Simulation; Velocity; High entropy alloys; Machine learning assisted molecular dynamics simulation; Optimum HEAs with conflicting objectives; Nonparametric kernel-based probabilistic machine learning; High-velocity impact; AlCoCrFeNi
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-62759-9

The large compositional space of high entropy alloys (HEA) often presents significant challenges in comprehensively deducing the critical influence of atomic composition on their mechanical responses. We propose an efficient nonparametric kernel-based probabilistic computational mapping to obtain the optimal composition of HEAs under ballistic conditions by exploiting the emerging capabilities of machine learning (ML) coupled with molecular-level simulations. Compared to conventional ML models, the present Gaussian approach is a Bayesian paradigm that can have several advantages, including small training datasets concerning computationally intensive simulations and the ability to provide uncertainty measurements of molecular dynamics simulations therein. The data-driven analysis reveals that a lower concentration of
Ni
with a higher concentration of
Al
leads to higher dissipation of kinetic energy and lower residual velocity, but with higher penetration depth of the projectile. To deal with such conflicting computationally intensive functional objectives, the ML-based simulation framework is further extended in conjunction with multi-objective genetic algorithm for identifying the critical elemental compositions to enhance kinetic energy dissipation with minimal penetration depth and residual velocity of the projectile simultaneously. The computational framework proposed here is generic in nature, and it can be extended to other HEAs with a range of non-aligned multi-physical property demands.